NeuroMotor PenTM Report

Published

March 10, 2023

Parkinson’s Disease Evaluation

Important

This report is intended to assist qualified Health Care Profesionals (HCP) in the assessment of an individual referred under the suspicion of having Parkinson’s Disease.

Clinical Context

This report presents several AI metrics derived from objective measures from individuals performing a battery of test using Manus Neurodynamica NeuroMotor PenTM.

These presented metrics combine factors from detailed measurement recordings made whilst the individual performs a battery of well established neurological test tasks.

The metrics have been assessed in a UK reference population and an individual’s results are presented in this clinical context. The HCP should review Clinical,Reference, Study et al [1] to establish applicability and limitations.

The information in this report should be used in the context of a full neurological assessment conducted to the current standard of care practices to establish a diagnosis.

Subject and Recording Details

Subject ID pg36
Test date and time 2018-01-26 10:34
Test battery template_05_subset_00, template_02_subset_00, Template_elel, template_06_subset_00, template_06_subset_04, template_04_subset_00, template_04_subset_00_offset, template_00_subset_00 (format)

Overall Assessment

PD

Subject performance similar to PD population (recommend review of report details)

Clinical context

In the clinical reference population, 41 individuals with a value less than 0.85 were subsequently diagnosed with PD. That is, 97.62% of the PD diagnoses in the study.

Additionally, 0 individuals with a value greater or equal to 0.85 were subsequently diagnosed as non PD. That is, 0.0% of the non PD diagnoses in the study.

Symptom Scores

These mini boxplots show the scores in a clinical context. Currently against the ‘Walker study’ data. A bigger pool would be much better (so max 83 individuals, usually lower if raw data did not result in successful classification).

Micrographia

The micrographia symptom assessment is derived from a combination of factors in the elel task.

{'FN': 19, 'TN': 13, 'TP': 23, 'FP': 17}

Tremor

The tremor score is a combination of features in the circle, spiral and both zizag tasks.

{'FN': 4, 'TN': 5, 'TP': 38, 'FP': 25}

Bradykinesia

The bradykinesia score is a combination of features in the circle, spiral, both zizag and elel tasks.

{'FN': 9, 'TN': 8, 'TP': 33, 'FP': 22}

Spatial Accuracy

The accuracy score is a combination of factors in the spiral, both zigzags and both Fitts tasks.

{'FN': 42, 'TN': 30, 'TP': 0, 'FP': 0}

Test Battery Details

2023-03-10 07:57:44.481 | INFO     | neuromotor_pen.data:_parse_header:698 - Loading Old Data Version...
ManusData_pg36.JSON

we need the signals file

Circle

/usr/local/lib/python3.9/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide
  c /= stddev[:, None]
/usr/local/lib/python3.9/site-packages/numpy/lib/function_base.py:2855: RuntimeWarning: invalid value encountered in divide
  c /= stddev[None, :]

Circle Segment 1

Duration 9.48 s, Accuracy Estimate 3.898 (lower is better)

Circle Segment 2

Duration 8.56 s, Accuracy Estimate 4.122 (lower is better)

Circle Segment 3

Duration 7.92 s, Accuracy Estimate 4.383 (lower is better)

Circle Segment 4

Duration 7.44 s, Accuracy Estimate 5.246 (lower is better)

Circle Segment 5

Duration 7.16 s, Accuracy Estimate 5.585 (lower is better)

Spiral

Spiral Segment 1

Duration 21.0 s, Accuracy Estimate 6.557 (lower is better)

Spiral Segment 2

Duration 29.32 s, Accuracy Estimate 2.716 (lower is better)

Spiral Segment 3

Duration 32.28 s, Accuracy Estimate 2.649 (lower is better)

Spiral Segment 4

Duration 28.36 s, Accuracy Estimate 2.814 (lower is better)

Spiral Segment 5

Duration 25.2 s, Accuracy Estimate 2.786 (lower is better)

Spiral Segment 6

Duration 30.72 s, Accuracy Estimate 3.045 (lower is better)

Spiral Segment 7

Duration 28.04 s, Accuracy Estimate 2.967 (lower is better)

Spiral Segment 8

Duration 25.2 s, Accuracy Estimate 2.617 (lower is better)

Spiral Segment 9

Duration 24.04 s, Accuracy Estimate 2.695 (lower is better)

Elel

Elel Segment 1

Elel Segment 2

Elel Segment 3

Elel Segment 4

Elel Segment 5

Elel Segment 6

Elel Segment 7

Elel Segment 8

Elel Segment 9

Elel Segment 10

Elel Segment 11

FITTS Short Modified

Can’t do fitts_short because low (or drifting) correlation between pressure and force

FITTS Long Modified

Can’t do fitts_long because low (or drifting) correlation between pressure and force

ZigZag

ZigZag Segment 1

Duration 15.08 s, Accuracy Estimate 5.399 (lower is better)

ZigZag Segment 2

Duration 14.52 s, Accuracy Estimate 4.401 (lower is better)

ZigZag Segment 3

Duration 15.24 s, Accuracy Estimate 3.938 (lower is better)

ZigZag Segment 4

Duration 17.72 s, Accuracy Estimate 4.511 (lower is better)

ZigZag Segment 5

Duration 18.72 s, Accuracy Estimate 2.957 (lower is better)

ZigZag Offset

ZigZag Offset Segment 1

Duration 13.68 s, Accuracy Estimate 0.038 (lower is better)

ZigZag Offset Segment 2

Duration 13.56 s, Accuracy Estimate 0.059 (lower is better)

ZigZag Offset Segment 3

Duration 10.96 s, Accuracy Estimate 0.07 (lower is better)

ZigZag Offset Segment 4

Duration 10.76 s, Accuracy Estimate 0.047 (lower is better)

ZigZag Offset Segment 5

Duration 11.56 s, Accuracy Estimate 0.04 (lower is better)

sentence

Can’t do sentence because Missing tablet or pen data

/usr/local/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.
  return _methods._mean(a, axis=axis, dtype=dtype,
/usr/local/lib/python3.9/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)

Appendices

Misc

Currently a dumping ground for things that could be included or previous output style.

Note

Putting all the results out here but will not be in a final report.

HiSpec {‘HiSpec_class’: ‘NOT PD’, ‘HiSpec_score’: 0.67}
RanFor {‘RanFor_class’: ‘PD’, ‘RanFor_score’: 0.85}
BM_May22 {‘BM_May22_class’: ‘NOT PD’, ‘BM_May22_score’: 0.5302306733632567}
BM_HC_Sep22 {‘BM_HC_Sep22_class’: ‘Patient’, ‘BM_HC_Sep22_score’: 0.934793936423459}
BM_PD_Sep22 {‘BM_PD_Sep22_class’: ‘PD’, ‘BM_PD_Sep22_score’: 0.7901971247215468}